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Record W7014064429

Oral History Interview with William Wareing, April 13, 2009

2009· article· en· W7014064429 on OpenAlexaboutno aff

Bibliographic record

VenueThe Portal to Texas History (University of North Texas) · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOral historyWorld War IIChoseOrder (exchange)Spanish Civil War
DOInot available

Abstract

fetched live from OpenAlex

The National Museum of the Pacific War presents an oral interview with William Wareing. Wareing attended the Hawken School as a child, impressing upon him the value of discipline. He later joined the ROTC and the Kentucky National Guard. With war looming, he applied both to the Army Air Corps and Royal Canadian Air Force. Accepted by both, he chose the Air Corps, completing flight training in December 1941 as a second lieutenant. After two years as an instructor, he was appointed to oversee curriculum at various flight schools. By that time, he was a captain and he turned down a promotion to major in favor of attending B-29 school. He then joined the 500th Bombardment Group, flying exactly one mission, the final bombardment of Japan, days after the second atomic bomb was dropped. Under antiaircraft fire, Wareing risked being court-martialed to break formation and ensure proper targeting. Following the war, Wareing dropped supplies over POW camps in Formosa and China. When one of his flights was diverted, he came so close to crashing into a mountainside that he caught a leaf in his landing gear. In November 1945 he was discharged in order to see his dying mother. Wareing then went on to join the reserves, retiring as a major, and served as a board member of the National Museum of the Pacific War.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0740.015

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.199
Teacher spread0.163 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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